# Can I run Mixtral 8x22B on a Mac Studio 256GB? > Yes, but it is tight. Mixtral 8x22B at Q8_0 needs about 172.8 GB; Mac Studio 256GB has 192.0 GB usable. Estimated tokens/sec and context headroom. Source: https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-studio-m5-ultra-256/ [Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Mixtral 8x22B](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/) / Mixtral 8x22B on a Mac Studio 256GB **Yes, but it is tight.** Mixtral 8x22B at Q8\_0 needs about 172.8 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable at a \~4k context. # Can I run Mixtral 8x22B on a Mac Studio 256GB? Mixtral 8x22B at Q8\_0 needs about 172.8 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable. That figure is 256 GB of unified memory on the spec sheet, of which 192.0 GB is usable for weights and cache, at 1200 GB/s memory bandwidth. Mixtral 8x22B, a large Mixture-of-Experts. Total weights need a high-memory box; only a fraction is active per token. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks. Quant that fits Q8\_0 Fits, short context only Memory needed 172.8 GB at Q8\_0, \~4k context Usable unified memory 192.0 GB of 256 GB unified memory Est. tokens/sec up to 23 estimate [Mixtral-8x22B-Instruct-v0.1](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/) · [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-studio-m5-ultra-256/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/) ## Memory at each quant | Quant | Need vs 192.0 GB | Verdict | | -------- | ---------------- | ------------- | | Q4\_K\_M | 99.0 GB | Q4Fits | | Q8\_0 | 172.8 GB | Q8Tight | | FP16 | 324.4 GB | FP16Won’t fit | ## Estimated tokens per second Estimate: **up to 23 tokens/sec** at Q8\_0 (upper bound: this is a Mixture-of-Experts, and only the active weights are counted). This is a bandwidth-derived range, not a bench. Efficiency is a 45–80% unified-memory band from [How we calculate](https://whatcanirunai.vercel.app/method/). ## Context headroom At Q8\_0, this machine has headroom out to about **64k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache. __Context headroom for Mixtral 8x22B on a Mac Studio 256GB at Q8\_0__ | Context | KV cache (FP16) | Total at Q8\_0 | Verdict | | ------- | --------------- | -------------- | --------- | | 4k | 0.9 GB | 172.8 GB | Tight | | 8k | 1.9 GB | 173.8 GB | Tight | | 16k | 3.8 GB | 175.7 GB | Tight | | 32k | 7.5 GB | 179.4 GB | Tight | | 64k | 15.0 GB | 186.9 GB | Tight | | 128k | 30.1 GB | 202.0 GB | Won’t fit | From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/). ## What to do instead * Run [gpt-oss-120b](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-256/) instead (82.5 GB at Q4). It is a different model, not a drop-in, but it does fit this machine. * Keep context short, close other GPU work, and avoid a higher quant. The [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) shows how the KV cache grows. * Full hardware list: [Mixtral-8x22B-Instruct-v0.1](https://whatcanirunai.vercel.app/models/mixtral-8x22b-instruct-v0.1/). Specs for this machine: [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/). ## Other hardware for Mixtral 8x22B * [Mixtral 8x22B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/rtx-4090/)Won’t fit * [Mixtral 8x22B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/rtx-5090/)Won’t fit * [Mixtral 8x22B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/dual-rtx-3090/)Won’t fit * [Mixtral 8x22B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-mini-m5-pro-64/)Won’t fit ## Other models on a Mac Studio 256GB * [Llama 3.3 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-studio-m5-ultra-256/)Fits * [R1 Distill 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-studio-m5-ultra-256/)Fits * [gpt-oss-120b on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-256/)Fits * [Qwen3-235B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-256/)Fits * [DeepSeek-R1 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/mac-studio-m5-ultra-256/)Won’t fit * [DeepSeek-V3.2 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/mac-studio-m5-ultra-256/)Won’t fit ## Quick answers Can I run Mixtral 8x22B on a Mac Studio 256GB? Yes, but it is tight. Mixtral 8x22B at Q8\_0 needs about 172.8 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable. How much VRAM does Mixtral 8x22B need? About 99.0 GB at Q4\_K\_M, 172.8 GB at Q8\_0 and 324.4 GB at FP16, estimated for a \~4k context. How fast is Mixtral 8x22B on a Mac Studio 256GB? Our estimate is up to 23 tokens per second at Q8\_0, as an upper bound for this MoE. It is not a measured benchmark. What context length can I use? At Q8\_0, our KV estimate supports up to about 64k tokens before the cache pushes the total over usable memory. --- Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/